Vehicle trajectory prediction is crucial for intelligent transportation systems (ITS) to improve traffic efficiency, alleviate congestion, and enhance safety. Despite advancements in deep learning, trajectory prediction faces challenges due to dynamic agent interactions and complex traffic environments. Traditional rasterized approaches suffer from high computational costs and information loss, prompting a shift towards vectorized methods that use Graph Neural Networks (GNNs) for better feature learning and robustness. We propose a lightweight model using dynamic graph neural networks (DGNN) to improve efficiency, interpretability, and accuracy. Evaluated on the Argoverse 2 dataset, our approach demonstrates reduced computational time, lower parameter count, and high prediction accuracy compared to baseline models. Additionally, we explore a Detection Transformer (DETR)-based
